---
title: "Coronavirus Dashboard"
author: ""
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
    theme: cerulean
---


```{r setup, include = F}

# Downloading data from Github Repository

download.file("https://github.com/RamiKrispin/coronavirus/raw/master/data/coronavirus.rda", destfile = "cvirus")
load("cvirus")

coronavirus$type[coronavirus$type == "recovery"] <- "recovered"

# Calling packages
suppressPackageStartupMessages(c(library(flexdashboard),
library(shinythemes),
library(coronavirus),
library(covid19italy),
library(tidyverse),
library(plotly),
library(maps),
library(leaflet),
library(leafpop),
library(purrr),
library(echarts4r),
library(knitr),
library(DT)))


# COLORS:

# A list of colors is available at the following link - http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf

# Defining color palette - also used for value box colors
confirmed_color <- "gray"
active_color <- "gold"
recovered_color <- "#6cc71c"
death_color <- "#f52525"


# Creating Datasets :-

# confirmed/active/recovered/death cases by country - old version

df <- coronavirus %>% 
  group_by(country, type) %>%
  summarise(total = sum(cases)) %>%
  pivot_wider(names_from =  type, 
                     values_from = total) %>%
  mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  arrange(-confirmed) %>%
  ungroup() %>%
  mutate(country = if_else(country == "United Arab Emirates", "UAE", country)) %>%
  mutate(country = if_else(country == "Mainland China", "China", country)) %>%
  mutate(country = if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  mutate(country = if_else(country == "Iran (Islamic Republic of)", "Iran", country)) %>%
  mutate(country = if_else(country == "Republic of Korea", "South Korea", country)) %>%
  mutate(country = if_else(country == "Russian Federation", "Russia", country)) %>%
  mutate(country = if_else(country == "Korea, South", "South Korea", country)) %>%
  mutate(country = if_else(country == "Dominican Repolic", "Dominican", country)) %>%
  mutate(country = trimws(country)) %>%
  mutate(country = factor(country)) %>%
  mutate(country = factor(country, levels = unique(country)))


# India specific dataset
df_india <- df %>%
  filter(country == "India")

# US specific dataset
df_us <- df %>%
  filter(country == "US")

# UK specific dataset
df_uk <- df %>%
  filter(country == "United Kingdom")

# Cumulative Cases :-
df_daily <- coronavirus %>% 
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = T)) %>%
  tidyr::pivot_wider(names_from = type,
                     values_from = total) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active =  confirmed - death - recovered) %>%
  dplyr::mutate(confirmed_cumulative = cumsum(confirmed),
         active_cumulative = cumsum(active),
         recovered_cumulative = cumsum(recovered),
         death_cumulative = cumsum(death))

df1 <- coronavirus %>% 
  filter(date == max(date))

#------------ Trajectory plot data prep ------------

df_china <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "China") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(china = cumsum(cases)) %>%
  dplyr::filter(china > 100)  %>%
  dplyr::select(-cases, -date)

df_china$index <- 1:nrow(df_china)


df_ind <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "India") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(india = cumsum(cases)) %>%
  dplyr::filter(india > 100)  %>%
  dplyr::select(-cases, -date)

df_ind$index <- 1:nrow(df_ind)


df_pakistan <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "Pakistan") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(pakistan = cumsum(cases)) %>%
  dplyr::filter(pakistan > 100)  %>%
  dplyr::select(-cases, -date)

df_pakistan$index <- 1:nrow(df_pakistan)


df_russia <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "Russia") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(russia = cumsum(cases)) %>%
  dplyr::filter(russia > 100)  %>%
  dplyr::select(-cases, -date)

df_russia$index <- 1:nrow(df_russia)


df_canada <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "Canada") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(canada = cumsum(cases)) %>%
  dplyr::filter(canada > 100)  %>%
  dplyr::select(-cases, -date)

df_canada$index <- 1:nrow(df_canada)


df_australia <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "Australia") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(australia = cumsum(cases)) %>%
  dplyr::filter(australia > 100)  %>%
  dplyr::select(-cases, -date)

df_australia$index <- 1:nrow(df_australia)


df_unitedkingdom <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "United Kingdom") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(unitedkingdom = cumsum(cases)) %>%
  dplyr::filter(unitedkingdom > 100)  %>%
  dplyr::select(-cases, -date)

df_unitedkingdom$index <- 1:nrow(df_unitedkingdom)


df_france <- coronavirus %>% dplyr::filter(type == "confirmed", country == "France") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(france = cumsum(cases)) %>%
  dplyr::filter(france > 100)  %>%
  dplyr::select(-cases, -date)

df_france$index <- 1:nrow(df_france)

df_unitedstates <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "US") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(unitedstates = cumsum(cases)) %>%
  dplyr::filter(unitedstates > 100)  %>%
  dplyr::select(-cases, -date)

df_unitedstates$index <- 1:nrow(df_unitedstates)

df_iran <- coronavirus %>% 
  dplyr::filter(type == "confirmed", country == "Iran") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(iran = cumsum(cases)) %>%
  dplyr::filter(iran > 100)  %>%
  dplyr::select(-cases, -date)

df_iran$index <- 1:nrow(df_iran)

df_sk <- coronavirus %>% dplyr::filter(type == "confirmed", country == "Korea, South") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(sk = cumsum(cases)) %>%
  dplyr::filter(sk > 100)  %>%
  dplyr::select(-cases, -date)

df_sk$index <- 1:nrow(df_sk)

df_spain <- coronavirus %>% dplyr::filter(type == "confirmed", country == "Spain") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(spain = cumsum(cases)) %>%
  dplyr::filter(spain > 100)  %>%
  dplyr::select(-cases, -date)

df_spain$index <- 1:nrow(df_spain)


df_italy <- italy_total %>% dplyr::select(date, italy = cumulative_cases) %>%
  dplyr::filter(italy > 100) %>%
  dplyr::select(-date)

df_italy$index <- 1:nrow(df_italy)

df_trajectory <- df_china %>% 
  dplyr::left_join(df_ind, by = "index") %>%
  dplyr::left_join(df_pakistan, by = "index") %>%
  dplyr::left_join(df_australia, by = "index") %>%
  dplyr::left_join(df_canada, by = "index") %>%
  dplyr::left_join(df_russia, by = "index") %>%
  dplyr::left_join(df_italy, by = "index") %>%
  dplyr::left_join(df_iran, by = "index") %>%
  dplyr::left_join(df_sk, by = "index") %>%
  dplyr::left_join(df_unitedstates, by = "index") %>%
  dplyr::left_join(df_france, by = "index") %>%
  dplyr::left_join(df_unitedkingdom, by = "index") %>%
  dplyr::left_join(df_spain, by = "index")


# Recoding Country names per their common names
df_map <- df %>%
  dplyr::mutate(country = recode_factor(country,
                                 `US` = "United States",
                                 `UK` = "United Kingdom",
                                 `UAE` = "United Arab Emirates",
                                 `South Korea` = "Korea"))

```



Dashboard
=======================================================================

Row
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "), 
         caption = "Total Confirmed Cases", 
         icon = "fas fa-user-md",
         color = confirmed_color)
```


### active {.value-box}

```{r}

valueBox(value = paste(format(sum(df$unrecovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df$unrecovered, na.rm = T) / sum(df$confirmed), 1),
                       "%)", sep = ""),
         caption = "Active Cases",
         icon = "fas fa-ambulance",
         color = active_color)
```

### recovered {.value-box}

```{r}

valueBox(value = paste(format(sum(df$recovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df$recovered, na.rm = T) / sum(df$confirmed), 1),
                       "%)", sep = ""),
         caption = "Recovered Cases",
         icon = "fas fa-heartbeat",
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(sum(df$death, na.rm = T), big.mark = ","), 
                       " (", round(100 * sum(df$death, na.rm = T) / sum(df$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-bed",
         color = death_color)

```


Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Confirmed Cases

```{r}

df_map %>%
  e_charts(country) %>%
  e_map(confirmed) %>%
  e_title("Confirmed Cases by Country", left = "center") %>%
  e_visual_map() %>%
  e_theme("infographic") %>%
  e_visual_map_range(selected = list(0,200))
  

```

### Active Cases

```{r}

df_map %>%
  e_charts(country) %>%
  e_map(unrecovered) %>%
  e_title("Active Cases by Country", left = "center") %>%
  e_visual_map() %>%
  e_theme("infographic") %>%
  e_visual_map_range(selected = list(0,200))


```

### Recovered Cases

```{r}

df_map %>%
  e_charts(country) %>%
  e_map(recovered) %>%
  e_title("Recovered Cases by Country", left = "center") %>%
  e_visual_map() %>%
  e_theme("infographic") %>%
  e_visual_map_range(selected = list(0,200))


```

### Death Cases 

```{r}

df_map %>%
  e_charts(country) %>%
  e_map(death) %>%
  e_title("Death Cases by Country", left = "center") %>%
  e_visual_map() %>%
  e_theme("infographic") %>%
  e_visual_map_range(selected = list(0,200))
  

```



Map
=======================================================================

**Map**

```{r map}

cv_data_for_plot <- coronavirus %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(country, province, lat, long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()

cv_data_for_plot.split <- cv_data_for_plot %>%
  split(cv_data_for_plot$type)

pal <- colorFactor(c(active_color, death_color, recovered_color), domain = c("confirmed", "death", "recovered"))

map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(data = cv_data_for_plot.split[[df]],
                 lng = ~long, lat = ~lat,
                 color = ~pal(type),
                 stroke = F,
                 fillOpacity = 0.8,
                 radius = ~log_cases,
                 popup =  popupTable(cv_data_for_plot.split[[df]],
                                              feature.id = F,
                                              row.numbers = F,
                                              zcol = c("type", "cases", "country", "province")),
                 group = df,
                 labelOptions = labelOptions(noHide = F,
                                             direction = 'auto'))
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = F)
  )

```



Region-wise
=======================================================================

Column 
-------------------------------------
    
### 
    
```{r}

df_table <- df %>%
  select(country, confirmed, death)

df_table %>%
  datatable(rownames = F)

```
   
   
### Locations of people killed by COVID-19

```{r, fig.width = 10, fig.height = 7}

world_map <- map_data("world")


coronavirus_maps <- coronavirus %>%
  filter(coronavirus$type == "death")

world_map %>%
  ggplot() +
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "gray",
               color = "gold") +
  geom_point(data = coronavirus_maps, 
             aes(x = coronavirus_maps$long,
             y = coronavirus_maps$lat,
             color = death_color,
             alpha = 0.7,
             size = 1.2)) +
  theme_classic() + 
  theme(
    axis.line = element_blank(),
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none"
  ) 

```
   
Column
-------------------------------------
   
### 

```{r}

plotly::plot_ly(data = df,
                x = ~ country,
                y = ~ confirmed,
                type = "bar",
                name = "Confirmed",
                marker = list(color = active_color)) %>%
  plotly::add_trace(y = ~ recovered,
                    name = "Recovered",
                    marker = list(color = recovered_color)) %>%
  plotly::add_trace(y = ~ death,
                    name = "Death",
                    marker = list(color = death_color)) %>%
  plotly::layout(barmode = 'stack',
                 yaxis = list(title = "Total Cases (log)",
                              type = "log"),
                 xaxis = list(title = "", tickangle = 30),
                 hovermode = "compare")

```
 
 
 
Visualisation
=======================================================================

Column
------------------------------------- 

### Daily New Cases - China vs. Rest of the World
    
```{r}

daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::mutate(country = dplyr::if_else(country == "India", 
                                         "India", if_else(country == "US", 
                                                          "US", if_else(country == "Italy", 
                                                                        "Italy", if_else(country == "Spain", 
                                                                                         "Spain", if_else(country == "China", 
                                                                                                          "China", "Rest of the World")))))) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>% 
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total) 


# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ India, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "India") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ US, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "US") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ Italy, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "Italy") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ Spain, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "Spain") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ China, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "China") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ `Rest of the World`, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "Rest of the World") %>% 
  plotly::add_annotations(x = as.Date("2020-02-13"),
                          y = 15133,
                          text = paste("        One time adjustment -", 
                                       "", 
                                       "China modified the diagnostic criteria"),
                          xref = "x",
                          yref = "y",
                          arrowhead = 5,
                          arrowhead = 3,
                          arrowsize = 1,
                          showarrow = TRUE,
                          ax = 40,
                          ay = -40) %>%
  plotly::add_annotations(x = as.Date("2020-02-26"),
                          y = 577,
                          text = paste("New cases outside of China surpass the ones inside China"),
                          xref = "x",
                          yref = "y",
                          arrowhead = 5,
                          arrowhead = 3,
                          arrowsize = 1,
                          showarrow = TRUE,
                          ax = 40,
                          ay = -60) %>%
  plotly::layout(title = "",
                 legend = list(x = 0.1, y = 0.9),
                 yaxis = list(title = "Number of New Cases"),
                 xaxis = list(title = "Date"),
                 hovermode = "compare",
                 margin =  list(b = 10,
                                t = 10,
                                pad = 2))

```



Trends
=======================================================================

Column
------------------------------------- 

### Recovery and Death Rates for Countries with at Least 25k confirmed cases

```{r}

coronavirus %>% 
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::filter(confirmed >= 25000) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
                death_rate = death / confirmed) %>% 
  dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
                death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
  plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
                  x = ~ round(100 * death_rate, 1),
                  size = ~  log(confirmed),
                  sizes = c(5, 70),
                  type = 'scatter', mode = 'markers',
                  color = ~ country,
                  marker = list(sizemode = 'diameter' , opacity = 0.5),
                  hoverinfo = 'text',
                  text = ~paste("", country, 
                                " Confirmed Cases: ", confirmed,
                                " Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
                                " Death Rate: ",  paste(round(100 * death_rate, 1), "%", sep = ""))
                 ) %>%
  plotly::layout(yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
  
```   
 

Column
------------------------------------- 

### Trajectory Plot - Major Countries 

```{r}

plotly::plot_ly(data = df_trajectory) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ china,
                    line = list(color = "darkorange", width = 2),
                    name = "China") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ india,
                    line = list(color = "navy", width = 2),
                    name = "India") %>%  
  plotly::add_lines(x = ~ index,
                    y = ~ pakistan,
                    line = list(color = "darkgreen", width = 2),
                    name = "Pakistan") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ russia,
                    line = list(color = "dodgerblue1", width = 2),
                    name = "Russia") %>%  
  plotly::add_lines(x = ~ index,
                    y = ~ canada,
                    line = list(color = "firebrick1", width = 2),
                    name = "Canada") %>%   
  plotly::add_lines(x = ~ index,
                    y = ~ australia,
                    line = list(color = "gold", width = 2),
                    name = "Australia") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ italy,
                    line = list(color = "hotpink", width = 2),
                    name = "Italy") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ unitedstates,
                    line = list(color = "black", width = 2),
                    name = "United States") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ unitedkingdom,
                    line = list(color = "coral4", width = 2),
                    name = "United Kingdom") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ france,
                    line = list(color = "greenyellow", width = 2),
                    name = "France") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ iran,
                    line = list(color = "dodgerblue4", width = 2),
                    name = "Iran") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ sk,
                    line = list(color = "brown", width = 2),
                    name = "South Korea") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ spain,
                    line = list(color = "blueviolet", width = 2),
                    name = "Spain") %>%
  plotly::layout(yaxis = list(title = "Cumulative Positive Cases",type = "log"),
                 xaxis = list(title = "Days since the total positive cases surpass 100"),
                 legend = list(x = 0.7, y = 0.3),
                 hovermode = "compare")
                 
```


### Treemap Plot 

```{r}

conf_df <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total_cases = sum(cases)) %>%
  arrange(-total_cases) %>%
  mutate(parents = "Confirmed") %>%
  ungroup() 
  
  plot_ly(data = conf_df,
          type = "treemap",
          values = ~ total_cases,
          labels = ~ country,
          parents =  ~ parents,
          domain = list(column = 0),
          name = "Confirmed",
          textinfo = "label + value + percent parent")
          
```

Key Data & Plots
=======================================================================


Row {data-width = 400}
-----------------------------------------------------------------------

### Cases Status Update for `r  max(coronavirus$date)`
    
```{r}

daily_summary <- coronavirus %>% 
  dplyr::filter(date == max(date)) %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::select(country = country, confirmed, recovered, death)
  
  
  DT::datatable(data = daily_summary,
                rownames = FALSE,
                colnames = c("Country", "Confirmed", "Recovered", "Death"),
                options = list(pageLength = nrow(daily_summary), dom = 'tip'))
  
```


### Recovery and Death Rates by Country
    
```{r}

df_summary <-coronavirus %>% 
  dplyr::filter(country != "Others") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 25) %>%
  dplyr::select(country = country, confirmed, recovered, death) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
         death_rate = death / confirmed)  

df_summary %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            options = list(pageLength = nrow(df_summary), dom = 'tip')) %>%
  DT::formatPercentage("recover_rate", 2) %>%
  DT::formatPercentage("death_rate", 2) 

```

    
Row {data-width = 400}
-----------------------------------------------------------------------


### Daily Cumulative Cases by Type
    
```{r}

plotly::plot_ly(data = df_daily,
                x = ~ date,
                y = ~ active_cumulative, 
                name = 'Active', 
                fillcolor = active_color,
                type = 'scatter',
                mode = 'none', 
                stackgroup = 'one') %>%
  plotly::add_trace(y = ~ recovered_cumulative,
                    name = "Recovered",
                    fillcolor = recovered_color) %>%
  plotly::add_trace(y = ~ death_cumulative,
                    name = "Death",
                    fillcolor = death_color) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
  

```


### New Cases - Top 15 Countries (`r  max(coronavirus$date)`)
    
```{r}

max_date <- max(coronavirus$date)

coronavirus %>% 
  dplyr::filter(type == "confirmed", date == max_date) %>%
  dplyr::group_by(country) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  dplyr::arrange(-total_cases) %>%
  dplyr::mutate(country = factor(country, levels = country)) %>%
  dplyr::ungroup() %>%
  dplyr::top_n(n = 15, wt = total_cases) %>%
  plotly::plot_ly(x = ~ country,
                  y = ~ total_cases,
                  text = ~ total_cases,
                  textposition = 'auto',
                  type = "bar") %>%
  plotly::layout(yaxis = list(title = "Number of Cases"),
                 xaxis = list(title = "Country"),
                 margin =  list(
                   l = 10,
                   r = 10,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))

```


Cumulative Results
=======================================================================


Column
------------------------------------- 

### Cumulative Confirmed Cases

```{r}

plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed_cumulative,
                    type = "bar",
                    name = "Active",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::layout(yaxis = list(title = "Confirmed Cases (cumulative)"),
                 xaxis = list(title = ""))

```



### Cumulative Active Cases

```{r}

plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ active_cumulative,
                    type = "bar",
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
  plotly::layout(yaxis = list(title = "Active Cases (cumulative)"),
                 xaxis = list(title = ""))

```


Row
-------------------------------------------------------------------------

### Cumulative Recovered Cases

```{r}

plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ recovered_cumulative,
                    type = "bar",
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
  plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
                 xaxis = list(title = ""))

```



### Cumulative Death Cases

```{r}

plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ death_cumulative,
                    type = "bar",
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
  plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
                 xaxis = list(title = ""))

```


India
=======================================================================

Column 
-------------------------------------
    
### 
    
```{r, fig.height = 10}

df_India <- coronavirus %>% 
  filter(country == "India") %>%
  group_by(date, type) %>%
  summarise(total = sum(cases, na.rm = F)) %>%
  pivot_wider(names_from = type,
              values_from = total) %>%
  arrange(date) %>%
  ungroup()

df_India %>%
  datatable(rownames = F)

```
   
   
### Confirmed Cases in India

```{r, fig.width=10, fig.height=10}

plotly::plot_ly(data = df_India) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed,
                    type = "bar",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::layout(yaxis = list(title = "Confirmed Cases"),
                 xaxis = list(title = ""))

```
   
Column 
-------------------------------------
   
### confirmed {.value-box}

```{r}

valueBox(value = paste(format(sum(df_india$confirmed), big.mark = ","), "", sep = " "),  
         caption = "Confirmed Cases in India",
         icon = "fas fa-user-md",
         color = confirmed_color)
```


### active {.value-box}

```{r}

valueBox(value = paste(format(sum(df_india$unrecovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_india$unrecovered, na.rm = T) / sum(df_india$confirmed), 1),
                       "%)", sep = ""),
         caption = "Active Cases",
         icon = "fas fa-ambulance",
         color = active_color)
```

### recovered {.value-box}

```{r}

valueBox(value = paste(format(sum(df_india$recovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_india$recovered, na.rm = T) / sum(df_india$confirmed), 1),
                       "%)", sep = ""),
         caption = "Recovered Cases",
         icon = "fas fa-heartbeat",
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(sum(df_india$death, na.rm = T), big.mark = ","), 
                       " (", round(100 * sum(df_india$death, na.rm = T) / sum(df_india$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-bed",
         color = death_color)

```
 

US
=======================================================================

Column 
-------------------------------------
    
### 
    
```{r, fig.height = 10}

df_US <- coronavirus %>% 
  filter(country == "US") %>%
  group_by(date, type) %>%
  summarise(total = sum(cases, na.rm = F)) %>%
  pivot_wider(names_from = type,
              values_from = total) %>%
  arrange(date) %>%
  ungroup()

df_US %>%
  datatable(rownames = F)

```
   
   
### Confirmed Cases in US

```{r, fig.width = 10, fig.height = 10}

plotly::plot_ly(data = df_US) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed,
                    type = "bar",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::layout(yaxis = list(title = "Confirmed Cases"),
                 xaxis = list(title = ""))

```
   
Column 
-------------------------------------
   
### confirmed {.value-box}

```{r}

valueBox(value = paste(format(sum(df_us$confirmed), big.mark = ","), "", sep = " "), 
         caption = "Confirmed Cases in US",
         icon = "fas fa-user-md",
         color = confirmed_color)
```


### active {.value-box}

```{r}

valueBox(value = paste(format(sum(df_us$unrecovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_us$unrecovered, na.rm = T) / sum(df_us$confirmed), 1),
                       "%)", sep = ""),
         caption = "Active Cases",
         icon = "fas fa-ambulance",
         color = active_color)
```

### recovered {.value-box}

```{r}

valueBox(value = paste(format(sum(df_us$recovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_us$recovered, na.rm = T) / sum(df_us$confirmed), 1),
                       "%)", sep = ""),
         caption = "Recovered Cases",
         icon = "fas fa-heartbeat",
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(sum(df_us$death, na.rm = T), big.mark = ","), 
                       " (", round(100 * sum(df_us$death, na.rm = T) / sum(df_us$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-bed",
         color = death_color)

```
 


UK
=======================================================================

Column 
-------------------------------------
    
### 
    
```{r, fig.height = 10}

df_UK <- coronavirus %>% 
  filter(country == "United Kingdom") %>%
  group_by(date, type) %>%
  summarise(total = sum(cases, na.rm = F)) %>%
  pivot_wider(names_from = type,
              values_from = total) %>%
  arrange(date) %>%
  ungroup()

df_UK %>%
  datatable(rownames = F) 

```
   
   
### Confirmed Cases in UK

```{r, fig.width = 10, fig.height = 10}

plotly::plot_ly(data = df_UK) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed,
                    type = "bar",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::layout(yaxis = list(title = "Confirmed Cases"),
                 xaxis = list(title = ""))
                 
```
   
Column 
-------------------------------------
   
### confirmed {.value-box}

```{r}

valueBox(value = paste(format(sum(df_uk$confirmed), big.mark = ","), "", sep = " "), 
         caption = "Confirmed Cases in UK",
         icon = "fas fa-user-md",
         color = confirmed_color)
```


### active {.value-box}

```{r}

valueBox(value = paste(format(sum(df_uk$unrecovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_uk$unrecovered, na.rm = T) / sum(df_uk$confirmed), 1),
                       "%)", sep = ""),
         caption = "Active Cases",
         icon = "fas fa-ambulance",
         color = active_color)
```

### recovered {.value-box}

```{r}

valueBox(value = paste(format(sum(df_uk$recovered, na.rm = T), big.mark = ","),
                       " (", round(100 * sum(df_uk$recovered, na.rm = T) / sum(df_uk$confirmed), 1),
                       "%)", sep = ""),
         caption = "Recovered Cases",
         icon = "fas fa-heartbeat",
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(sum(df_uk$death, na.rm = T), big.mark = ","), 
                       " (", round(100 * sum(df_uk$death, na.rm = T) / sum(df_uk$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-bed",
         color = death_color)

```
 


About
=======================================================================

**The Coronavirus Dashboard**

This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the [Rmarkdown](https://rmarkdown.rstudio.com/) using [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) framework and can easily reproduce by others.

**Data**

The input data for this dashboard is the [coronavirus](https://github.com/RamiKrispin/coronavirus) R package. The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv)



**Packages**

* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package. 
* Data manipulation - [tidyverse](https://dplyr.tidyverse.org/)
* Tables - the [DT](https://rstudio.github.io/DT/) package
* Visualisation - the [plotly](https://plot.ly/r/) package for the plots, [echarts4r](https://github.com/JohnCoene/echarts4r), [leafpop](https://github.com/r-spatial/leafpop) and [leaflet](https://rstudio.github.io/leaflet/) for the maps displayed

**Deployment and reproducibility**

The dashboard can be deployed to Rpubs or to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:

* Fork the dashboard [repository](https://github.com/mehtashubham/covid19_dashbaord), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)

For any question or feedback, you can either open an [issue](https://github.com/mehtashubham/covid19_dashbaord/issues) or contact me on [LinkedIn](https://www.linkedin.com/in/mehta-shubham/).

**Contributions**

Special thanks John Ng and Zhan Liang Chan for guiding me through hosting this work on Github.io, and to all package developers who have contributed their bit in this development

**Dedication**

This dashboard has been created for usage by epidemiologists, data scientists and public in general. It is dedicated to all the health workers across the globe who are fighting this war from the front and impacting millions of lives with their great work. God bless them all. 

This too shall pass!